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LoweredProblem dataclass - container for all lowering outputs.

LoweredProblem dataclass

Container for all outputs from symbolic problem lowering.

This dataclass holds all the results of lowering symbolic expressions to executable JAX and CVXPy code. It provides a clean, typed interface for accessing the various components needed for optimization.

Attributes:

Name Type Description
dynamics Dynamics

Optimization dynamics with fields f, A, B (JAX functions)
dynamics_prop Dynamics

Propagation dynamics with fields f, A, B
jax_constraints LoweredJaxConstraints

Non-convex constraints lowered to JAX with gradients
cvxpy_constraints LoweredCvxpyConstraints

Convex constraints lowered to CVXPy
x_unified UnifiedState

Aggregated optimization state interface
u_unified UnifiedControl

Aggregated optimization control interface
x_prop_unified UnifiedState

Aggregated propagation state interface
ocp_vars CVXPyVariables

Typed CVXPy variables and parameters for OCP construction
cvxpy_params Dict[str, Parameter]

Dict mapping user parameter names to CVXPy Parameter objects
Example

After lowering a symbolic problem::

lowered = lower_symbolic_problem(
    dynamics_aug=dynamics,
    states_aug=states,
    controls_aug=controls,
    constraints=constraint_set,
    parameters=params,
    N=50,
)

# Access components
dx_dt = lowered.dynamics.f(x, u, node, params)
jacobian_A = lowered.dynamics.A(x, u, node, params)

# Use CVXPy objects
ocp = OptimalControlProblem(settings, lowered)
Source code in openscvx/lowered/problem.py 
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@dataclass
class LoweredProblem:
    """Container for all outputs from symbolic problem lowering.

    This dataclass holds all the results of lowering symbolic expressions
    to executable JAX and CVXPy code. It provides a clean, typed interface
    for accessing the various components needed for optimization.

    Attributes:
        dynamics: Optimization dynamics with fields f, A, B (JAX functions)
        dynamics_prop: Propagation dynamics with fields f, A, B
        jax_constraints: Non-convex constraints lowered to JAX with gradients
        cvxpy_constraints: Convex constraints lowered to CVXPy
        x_unified: Aggregated optimization state interface
        u_unified: Aggregated optimization control interface
        x_prop_unified: Aggregated propagation state interface
        ocp_vars: Typed CVXPy variables and parameters for OCP construction
        cvxpy_params: Dict mapping user parameter names to CVXPy Parameter objects

    Example:
        After lowering a symbolic problem::

            lowered = lower_symbolic_problem(
                dynamics_aug=dynamics,
                states_aug=states,
                controls_aug=controls,
                constraints=constraint_set,
                parameters=params,
                N=50,
            )

            # Access components
            dx_dt = lowered.dynamics.f(x, u, node, params)
            jacobian_A = lowered.dynamics.A(x, u, node, params)

            # Use CVXPy objects
            ocp = OptimalControlProblem(settings, lowered)
    """

    # JAX dynamics
    dynamics: Dynamics
    dynamics_prop: Dynamics

    # Lowered constraints (separate types for JAX vs CVXPy)
    jax_constraints: LoweredJaxConstraints
    cvxpy_constraints: LoweredCvxpyConstraints

    # Unified interfaces
    x_unified: UnifiedState
    u_unified: UnifiedControl
    x_prop_unified: UnifiedState

    # CVXPy objects
    ocp_vars: CVXPyVariables
    cvxpy_params: Dict[str, "cp.Parameter"]